Supporting data for "Using deep learning to quantify total scores of cerebral small vessel disease (CSVD) in a stroke cohort

Cerebral small vessels include the arterioles, capillaries, and venules in the brain, which are essential for controlling cerebral blood flow and maintaining brain homeostasis. Diseases related to these vessels are defined as cerebral small vessel disease (CSVD). CSVD has multiple clinical presentations, including cognitive impairment, gait disturbance, and acute ischemic stroke or transient ischemic attack. MRI features of CSVD include recent small subcortical infarct (RSSI), lacunes, enlarged perivascular space (EPVS), white matter hyperintensities (WMH), and cerebral microbleeds (CMB).

To evaluate the total CSVD load in patients, a score is applied to assess each subtype of CSVD. However, it is very tedious and time-consuming to label these by hand. This project proposed an auto pipeline for the computer-assisted detection of CSVD using deep learning on a large dataset of local stroke patients. A total number of 974 subjects—all of whom had been clinically diagnosed with transient ischemic attack or ischemic stroke—were recruited in this study. An external testing cohort comprising 48 stroke patients was also collected for this study. These patients all underwent scanning at the local MRI unit and all patients were well-informed about this research and provided signed consent. All the MRI data were processed under the standards of the local MRI unit.This thesis represents the first attempt to employ deep learning methods for the automated detection of total CSVD scores in a comparatively large cohort of stroke patients. The dataset utilized encompasses all subtypes of CSVD and was meticulously labeled by experienced clinical practitioners. This project provides a thorough and detailed application of deep learning detection on medical images and potentially opens avenues for robust applications in the field of AI-medicine.